iframe-proxy | Sunbelt Computer Software

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import OpenAI from 'openai';

import pg from 'pg';

import pgvector from 'pgvector/pg';

const client = new pg.Client({database: 'pgvector_example'});

await client.connect();

await client.query('CREATE EXTENSION IF NOT EXISTS vector');

await pgvector.registerTypes(client);

await client.query('DROP TABLE IF EXISTS documents');

await client.query('CREATE TABLE documents (id bigserial PRIMARY KEY, content text, embedding vector(1536))');

async function embed(input) {

const openai = new OpenAI();

const response = await openai.embeddings.create({input: input, model: 'text-embedding-3-small'});

return response.data.map((v) => v.embedding);

}

const input = [

'The dog is barking',

'The cat is purring',

'The bear is growling'

];

const embeddings = await embed(input);

for (let [i, content] of input.entries()) {

await client.query('INSERT INTO documents (content, embedding) VALUES ($1, $2)', [content, pgvector.toSql(embeddings[i])]);

}

const query = 'forest';

const queryEmbedding = (await embed([query]))[0];

const { rows } = await client.query('SELECT content FROM documents ORDER BY embedding <=> $1 LIMIT 5', [pgvector.toSql(queryEmbedding)]);

for (let row of rows) {

console.log(row.content);

}

await client.end();

Sunbelt Computer Software